# Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $150,029

## Abstract

Project Summary
Our prosed study will address critical gaps in the literature and practice of informed consent
in digital health research. We will leverage the existing Digital Health Checklist (DHC) tool by
expanding the consent prototype building component to incorporate what is meaningful to
research participants. This study involves co-designing a meaningful informed consent
prototype with participants to produce and test a digital health consent blueprint to increase
capacity for understanding the function of algorithms used in behavioral interventions. These
advances in the DHC tool will contribute to the evidence-base to support the process of
informing prospective participants about digital health research. This study will leverage an
established decision support tool developed for digital health researchers. The DHC was
informed through an iterative design process involving behavioral scientists, regulators, IRB
members, ethicists, and clinician-researchers and is grounded in accepted principles of
research ethics, namely respect for persons, beneficence and justice, and incorporates four
orthogonal domains including: (1) Access and Usability, (2) Risks and Benefits, (3) Privacy, and
(4) Data Management. Inspired by an effectiveness-implementation design process, we will
test and co-design an interactive consent form with prospective research participants. This
human centered participatory design approach will expose unique concerns when asked to use
a digital technology to gather personal health information. The proposed work will systematically
study and actively respond to critical ethical, legal/regulatory and social implications (ELSI)
applied to digital health research - specifically our ability to convey accessible study information
such that informed consent transpires. This research will directly benefit our parent R01, will
contribute to the literature on informed consent and have potential implications for other
personalization algorithms for behavior change, such as those used in industry. Co-designing
innovative decision support tools that can be used by researchers, algorithm developers, IRBs,
and participants will foster shared decision making at the earliest stages of digital health
research and algorithm creation.

## Key facts

- **NIH application ID:** 10367716
- **Project number:** 3R01CA244777-02S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Eric Hekler
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $150,029
- **Award type:** 3
- **Project period:** 2020-07-14 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10367716

## Citation

> US National Institutes of Health, RePORTER application 10367716, Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods (3R01CA244777-02S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10367716. Licensed CC0.

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